Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI:10.1146/annurev-biodatasci-102523-103821
Jarrod Shilts, Gavin J Wright
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Abstract

Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.

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绘制人类细胞表面相互作用组:解码细胞间通讯的一把钥匙
细胞表面的蛋白质是一个细胞与另一个细胞之间的物理连接点,可实现细胞间的直接交流和内聚结构。随着生物医学研究从描述单个细胞向了解人体的多细胞组织飞跃,细胞表面分子之间的结合相互作用对于计算模型和临床利用这些有影响力的受体通路都是至关重要的。为了实现这一更远大的愿景,我们必须汇集表面蛋白连接方式的全部相互作用组。本综述探讨了我们离了解人类细胞表面蛋白相互作用组还有多远。我们总结了用于组装表面蛋白相互作用组的数据库和系统技术的现状,同时强调了仍然存在的巨大差距。我们希望以此为路线图,最终建立一个更强大的人类细胞表面蛋白相互作用组图谱。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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The Evolutionary Interplay of Somatic and Germline Mutation Rates. Centralized and Federated Models for the Analysis of Clinical Data. Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication. Data Science Methods for Real-World Evidence Generation in Real-World Data. Graph Artificial Intelligence in Medicine.
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